import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


lowercase_  : int								=					logging.get_logger(__name__)







def       A__   (						snake_case_			:		Any							,					snake_case_			:		List[str]   ):
 SCREAMING_SNAKE_CASE__: List[str]=   set()
 SCREAMING_SNAKE_CASE__: int=   []

 def parse_line(snake_case_			:		List[str]   ):
  for line in fp:
   if isinstance(snake_case_							,					snake_case_   ):
    SCREAMING_SNAKE_CASE__: Dict=   line.decode('''UTF-8'''   )
   if "warnings summary (final)" in line:
    continue
   # This means we are outside the body of a warning
   elif not line.startswith(''' '''   ):
    # process a single warning and move it to `selected_warnings`.
    if len(snake_case_   ) > 0:
     SCREAMING_SNAKE_CASE__: List[str]=   '''\n'''.join(snake_case_   )
     # Only keep the warnings specified in `targets`
     if any(F': {x}: ' in warning for x in targets   ):
      selected_warnings.add(snake_case_   )
     buffer.clear()
    continue
   else:
    SCREAMING_SNAKE_CASE__: int=   line.strip()
    buffer.append(snake_case_   )

 if from_gh:
  for filename in os.listdir(snake_case_   ):
   SCREAMING_SNAKE_CASE__: Optional[Any]=   os.path.join(snake_case_							,					snake_case_   )
   if not os.path.isdir(snake_case_   ):
    # read the file
    if filename != "warnings.txt":
     continue
    with open(snake_case_   ) as fp:
     parse_line(snake_case_   )
 else:
  try:
   with zipfile.ZipFile(snake_case_   ) as z:
    for filename in z.namelist():
     if not os.path.isdir(snake_case_   ):
      # read the file
      if filename != "warnings.txt":
       continue
      with z.open(snake_case_   ) as fp:
       parse_line(snake_case_   )
  except Exception:
   logger.warning(
       F'{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'   )

 return selected_warnings







def       A__   (						snake_case_			:		Dict							,					snake_case_			:		Any   ):
 SCREAMING_SNAKE_CASE__: List[str]=   set()

 SCREAMING_SNAKE_CASE__: Tuple=   [os.path.join(snake_case_							,					snake_case_   ) for p in os.listdir(snake_case_   ) if (p.endswith('''.zip'''   ) or from_gh)]
 for p in paths:
  selected_warnings.update(extract_warnings_from_single_artifact(snake_case_							,					snake_case_   )   )

 return selected_warnings


if __name__ == "__main__":







      def       A__   (						snake_case_			:		int   ):
       return values.split(''','''   )

      lowercase_  : Dict								=					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
      parser.add_argument(
          '--output_dir',
          type=str,
          required=True,
          help='Where to store the downloaded artifacts and other result files.',
      )
      parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
      # optional parameters
      parser.add_argument(
          '--targets',
          default='DeprecationWarning,UserWarning,FutureWarning',
          type=list_str,
          help='Comma-separated list of target warning(s) which we want to extract.',
      )
      parser.add_argument(
          '--from_gh',
          action='store_true',
          help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
      )

      lowercase_  : Tuple								=					parser.parse_args()

      lowercase_  : List[Any]								=					args.from_gh
      if from_gh:
            # The artifacts have to be downloaded using `actions/download-artifact@v3`
            pass
      else:
            os.makedirs(args.output_dir, exist_ok=True)

            # get download links
            lowercase_  : Union[str, Any]								=					get_artifacts_links(args.workflow_run_id, token=args.token)
            with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
                  json.dump(artifacts, fp, ensure_ascii=False, indent=4)

            # download artifacts
            for idx, (name, url) in enumerate(artifacts.items()):
                  print(name)
                  print(url)
                  print('=' * 8_0)
                  download_artifact(name, url, args.output_dir, args.token)
                  # Be gentle to GitHub
                  time.sleep(1)

    # extract warnings from artifacts
      lowercase_  : Optional[Any]								=					extract_warnings(args.output_dir, args.targets)
      lowercase_  : Any								=					sorted(selected_warnings)
      with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
            json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)



"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE			  =  """cuda""" if torch.cuda.is_available() else """cpu"""



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=1_00		,   UpperCAmelCase_=" "					)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		text.split(UpperCAmelCase_					)
       return [character.join(text[i : i + n]					).strip() for i in range(0		,   len(UpperCAmelCase_					)		,   UpperCAmelCase_					)]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase			,							UpperCamelCase       			=		[], []
       for title, text in zip(documents["title"]		,   documents["text"]					):
              if text is not None:
                     for passage in split_text(UpperCAmelCase_					):
                            titles.append(title if title is not None else ""					)
                            texts.append(UpperCAmelCase_					)
       return {"title": titles, "text": texts}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase       			=		ctx_tokenizer(
           documents["title"]		,   documents["text"]		,   truncation=UpperCAmelCase_		,   padding="longest"		,   return_tensors="pt"					)["input_ids"]
       UpperCamelCase       			=		ctx_encoder(input_ids.to(device=UpperCAmelCase_					)		,   return_dict=UpperCAmelCase_					).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   )->				List[str]:







       """simple docstring"""
       ######################################
       logger.info("Step 1 - Create the dataset"					)
       ######################################

       # The dataset needed for RAG must have three columns:
       # - title (string): title of the document
       # - text (string): text of a passage of the document
       # - embeddings (array of dimension d): DPR representation of the passage
       # Let's say you have documents in tab-separated csv files with columns "title" and "text"
       assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

       # You can load a Dataset object this way
       UpperCamelCase       			=		load_dataset(
           "csv"		,   data_files=[rag_example_args.csv_path]		,   split="train"		,   delimiter="\t"		,   column_names=["title", "text"]					)

       # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

       # Then split the documents into passages of 100 words
       UpperCamelCase       			=		dataset.map(UpperCAmelCase_		,   batched=UpperCAmelCase_		,   num_proc=processing_args.num_proc					)

       # And compute the embeddings
       UpperCamelCase       			=		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase_					)
       UpperCamelCase       			=		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
       UpperCamelCase       			=		Features(
           {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
       UpperCamelCase       			=		dataset.map(
           partial(UpperCAmelCase_		,   ctx_encoder=UpperCAmelCase_		,   ctx_tokenizer=UpperCAmelCase_					)		,   batched=UpperCAmelCase_		,   batch_size=processing_args.batch_size		,   features=UpperCAmelCase_		,   )

       # And finally save your dataset
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset"					)
       dataset.save_to_disk(UpperCAmelCase_					)
       # from datasets import load_from_disk
       # dataset = load_from_disk(passages_path)  # to reload the dataset

       ######################################
       logger.info("Step 2 - Index the dataset"					)
       ######################################

       # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
       UpperCamelCase       			=		faiss.IndexHNSWFlat(index_hnsw_args.d		,   index_hnsw_args.m		,   faiss.METRIC_INNER_PRODUCT					)
       dataset.add_faiss_index("embeddings"		,   custom_index=UpperCAmelCase_					)

       # And save the index
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset_hnsw_index.faiss"					)
       dataset.get_index("embeddings"					).save(UpperCAmelCase_					)
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index



@dataclass
class 	__a	:
       UpperCamelCase_		:       str				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''						)     ,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/rag-sequence-nq'''     ,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/dpr-ctx_encoder-multiset-base'''     ,  metadata={
               '''help''': (
                   '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
                   ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
               )
           }     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb'''						)     ,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       Optional[int]				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
           }     ,  )
       UpperCamelCase_		:       int				=      field(
           default=16     ,  metadata={
               '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
           }     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       int				=      field(
           default=768     ,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}     ,  )
       UpperCamelCase_		:       int				=      field(
           default=128     ,  metadata={
               '''help''': (
                   '''The number of bi-directional links created for every new element during the HNSW index construction.'''
               )
           }     ,  )


if __name__ == "__main__":
      logging.basicConfig(level=logging.WARNING)
      logger.setLevel(logging.INFO)

      SCREAMING_SNAKE_CASE			  =  HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE			  =  parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE			  =  rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)





